Synchronization of Markov Jump Neural Networks With Communication Constraints via Asynchronous Output Feedback Control.

Abstract

This article is concerned with the synchronization issue of discrete Markov jump neural networks (MJNNs). First, to save communication resources, a universal communication model, including event-triggered transmission, logarithmic quantization, and asynchronous phenomenon, is proposed, which is close to the actual situation. Here, to further reduce conservatism, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. To cope with mode mismatch between the nodes and controllers due to potentially occurring time lag and packet dropouts, a hidden Markov model (HMM) method is adopted. Second, considering that state information of nodes may not be available, the asynchronous output feedback controllers are devised by a novel decoupling strategy. Then, sufficient conditions based on linear matrix inequalities (LMIs) for dissipative synchronization of MJNNs are proposed with the virtue of Lyapunov techniques. Third, by eliminating asynchronous terms, a corollary with less computational cost is devised. Finally, two numerical examples verify the effectiveness of the above results.